Matching the bouy data to the other data collected
hilo <- hbb_wku_h_xts
hilo <- data.frame(date=index(hilo), coredata(hilo))
hilo <- hilo[529:54816,]
hilo
# which(hilo$date=="2010-10-23 00:00:00") = index 529
# which(hilo$date=="2016-12-31 23:00:00") = index 54816
length(hilo[,1]) # we are left with 54288 lines of data
[1] 54288
54816 - length(hilo[,1]) # we lost 528 values
[1] 528
Changing column names
# removing columns that we are not using
hilo$date.2 <- NULL # another date column
hilo$date.1 <- NULL # another date column
hilo$BGARFU <- NULL # ?
hilo$cfs <- NULL
hilo$DOmgL <- NULL # dissolved oxygen
#hilo$Doper <- NULL # dissolved oxygen
hilo$PAR1 <- NULL # ?
hilo$pH <- NULL # pH
hilo$NTU <- NULL # a different measurement for turbitity
hilo$DOper10 <- NULL # dissolved oxygen
# colnames(hilo) <- c("Date", "cfs", "RiverFlow-cumec", "LogRiverFlow-cumec", "Chlorophyll-RFU", "Salinity-PPT", "Temp-C", "chlorophyll-calibrator", "Turbidity-NTU")
# does not work ???
hilo
====================================================
FULL DATA SET 2012-2016
Descriptives: PLots
River Flow FULL DATA SET
length(hilo$logcms[which(is.na(hilo$logcms)==TRUE)]) # 12 NAs
[1] 12
which(is.na(hilo$logcms)==TRUE)
[1] 50509 50510 50511 50512 50513 50514 50515 50516 50517 50518 50519
[12] 50520
RiverFlow <- ggplot(hilo, aes(x = date, y = as.numeric(cms))) +
geom_line()
print(RiverFlow + ggtitle("River Flow")+labs(x="Time", y = "River Flow - cubic meters per second"))

CHL FULL DATA SET
length(hilo$ChlRFU[which(is.na(hilo$ChlRFU)==TRUE)]) # 20464 NAs
[1] 20464
which(as.numeric(hilo$ChlRFU)==max(as.numeric(na.omit(hilo$ChlRFU)))) # 15.3 max
[1] 38974
# CHL tells us where in the data set this happened
hilo[38974,]
CHL <- ggplot(hilo, aes(x = date, y = as.numeric(ChlRFU))) +
geom_line()
print(CHL + ggtitle("Chlorophyll ")+labs(x="Time", y = "Chlorophyll - relative fluorescence units (RFU)"))

Turbity FULL DATA SET
length(hilo$Corr.NTU[which(is.na(hilo$Corr.NTU)==TRUE)]) #15012 NAs
[1] 15012
which(as.numeric(hilo$Corr.NTU)==max(as.numeric(na.omit(hilo$Corr.NTU)))) # 88.4
[1] 33243
# tells us where in the data set this happened
hilo[33243,]
TURB <- ggplot(hilo,aes(x = date, y = as.numeric(Corr.NTU))) +
geom_line()
print(TURB + ggtitle("Turbidity ")+labs(x="Time", y = "Turbidity - Nephelometric Turbidity Units (NTU)"))

Salinity FULL DATA SET
length(hilo$saltppt[which(is.na(hilo$saltppt)==TRUE)]) #11330 NAs
[1] 0
SALT <- ggplot(hilo, aes(x = date, y = as.numeric(saltppt))) +
geom_line()
print(SALT + ggtitle("Salinity")+labs(x="Time", y = "Salinity - unit parts per thousand (PPT)"))
Error in FUN(X[[i]], ...) : object 'saltppt' not found

======================================================== # Histograms FULL DATA SET ## River Flow Histogram
CHL Histogram
Turbity Histogram
Salinity Histogram
========================================================= # NEW DATA SET-Modified Data 2013-2015
# start date: 2013-01-01 00:00:00
# end date: 2015-12-31 23:00:00
hilomodified <- hilo[19225:45504,]
length(hilo[,1])-length(hilomodified[,1])
[1] 28008
# lost 28008 entries of data
length(hilomodified[,1])-528 # we are left with 25752 entries of data
[1] 25752
head(hilomodified)
tail(hilomodified)
NA
Descriptives on all variables MODIFIED DATA SET: Using Favstats
River Flow Favstats
CHL Favstats
Turbitity Favstats
Salinity Favstats
===================================================== # MODIFIED DATA SET 2013-2015 # Descriptives: Plots
River Flow MODIFIED
CHL MODIFIED
Turbitity MODIFIED
Salinity MODIFIED
Tempurature MODIFIED
Dissolved Oxygen MODIFIED
length(hilomodified$Doper[which(is.na(hilomodified$Doper)==TRUE)]) # 2267 NAs
TempMod <- ggplot(hilomodified, aes(x = date, y = as.numeric(Doper))) +
geom_line()
print(TempMod + ggtitle("Dissolved Oxygen")+labs(x="Time", y = "Dissolved Oxygen in percent of saturation"))
=================================================== # Histograms MODIFIED
River Flow MODIFIED
hist(as.numeric(hilomodified$cms), main = "Histogram of Log River Flow", xlab = "Log River Flow", breaks =90, xlim = c(0,100))

# this looks okay
CHL MODIFIED
Turbitity MODIFIED
Salinity MODIFIED
# skewed
hist(as.numeric(hilomodified$saltppt), main = "Histogram of Salinity", xlab = "Salinity - unit parts per thousand (PPT)")
# this is worst!
hist(log(as.numeric(hilomodified$saltppt)), main = "Histogram of Log Salinity", xlab = "Salinity")
# not sure what happens to units when taking the log
=============================================================
Plot with ALL Var 2013-2015
It is hard to see what is going on
============================================================== # Descriptives by Storm We are picking one storm from each year. We can indicate a storm has occurred by the extreme events in the river flow data. We will not use the log (which is logbase10) in order to see the extreme events When salinity is below 35 this also indicates a storm has occurred.
We will break the data set by year to find the most extreme event for each year.
2013 Data & Plot
Split 2013 into 6 months to get a better visual
2014 Data & Plot
2015 Data & Plot
# Separating the Data by Storm Events
Separating the Data by Storm
Trying to make the Rainfall plot easier to read
RiverFlow1 <- ggplot(hilomodified[1:100,], aes(x = date, y = as.numeric(cms))) +
geom_line()
print(RiverFlow + ggtitle("River Flow")+labs(x="Time", y = "River Flow - cubic meters per second"))
=======
NEW Change in River Flow Column
length(end)
[1] 72
for(i in 1:length(start)){
storm <- ggplot(hilomodified[(start[i]-24):(end[i]+24),], aes(x = date, y = as.numeric(cms))) +
geom_line(color="black")+
geom_line(aes(y = as.numeric(Corr.NTU)), color="grey69") +
geom_line(aes(y=as.numeric(ChlRFU)),color="khaki")
print(storm + ggtitle("Storm 1/7/13")+labs(x="Time"))
}








































































ChangeRF <- function(x = vector()){
change <- c(0)
for(i in 1:(length(x)-1)){
change[i+1] <- x[i]-x[i+1]
}
return(change)
}
change.vector <- c(ChangeRF(as.numeric(hilomodified$cms)))
change.vector
[1] 0.00000 0.02124 0.02832 0.02124 0.02832
[6] 0.00708 0.00000 0.02124 0.02124 0.02832
[11] 0.01416 0.00000 0.00708 0.00708 0.01416
[16] 0.02124 0.00708 0.00708 0.01416 0.03540
[21] 0.00000 0.01416 0.00000 0.02832 0.02124
[26] 0.02124 0.00000 0.01416 0.01416 0.00000
[31] 0.00000 0.00000 0.02832 -0.00708 0.02124
[36] 0.00000 0.01416 0.02124 0.00000 0.02124
[41] 0.01416 0.00000 0.00000 0.00708 0.00708
[46] 0.00708 0.02832 0.00708 0.00708 0.01416
[51] 0.00000 0.00708 0.00000 0.00000 0.02124
[56] 0.00000 0.01416 0.02124 -0.01416 0.01416
[61] 0.00000 0.00000 0.00708 0.02124 0.00708
[66] 0.00708 0.01416 0.00000 0.00000 0.00708
[71] 0.02832 0.00708 0.00708 0.00708 0.01416
[76] 0.00000 0.00000 -0.01416 0.01416 0.01416
[81] 0.00000 0.00000 0.00000 0.00000 0.00000
[86] -0.00708 -0.02832 -0.04248 -0.00708 0.00000
[91] -0.02124 -0.02832 -0.04248 -0.02124 -0.01416
[96] -0.02124 -0.02124 0.01416 0.03540 0.00708
[101] 0.00000 0.00708 0.00708 -0.19116 -0.25488
[106] -0.29028 -0.16284 0.09204 -0.33984 -0.44604
[111] 0.04956 0.16284 0.12036 -0.11328 -0.22656
[116] -0.27612 -0.31152 -1.13988 -1.58592 0.59472
[121] 0.67968 0.49560 0.14160 0.00708 0.21948
[126] 0.04248 -0.41064 -0.95580 -0.45312 0.32568
[131] 0.53100 0.54516 0.56640 0.32568 0.29736
[136] 0.06372 -0.45312 -0.82836 -0.63720 -1.22484
[141] -1.92576 -2.30100 -2.73996 -1.72044 -0.54516
[146] -0.25488 -0.09912 -0.43896 -1.32396 -1.54344
[151] -1.43724 -1.09032 -0.55224 -0.10620 0.44604
[156] 0.92748 1.83372 2.47092 2.03196 1.81248
[161] 1.60008 1.35228 1.06908 0.86376 0.75756
[166] 0.66552 0.54516 0.43188 0.43896 0.31860
[171] 0.26196 0.24780 0.19116 0.16992 0.09912
[176] 0.13452 0.04956 0.09912 0.02124 0.04956
[181] 0.02832 0.03540 0.00000 0.05664 0.02124
[186] 0.02832 0.02832 0.04248 -0.00708 0.00708
[191] -0.07788 -0.12036 -0.04956 -0.16284 -0.12036
[196] -0.21240 -0.20532 -0.25488 -0.80004 0.01416
[201] 0.05664 -0.24780 -0.41064 0.06372 0.24780
[206] 0.27612 0.22656 0.24780 0.12036 0.02832
[211] -0.35400 -1.84080 -5.09760 -6.35784 -10.52088
[216] -4.71528 3.39840 4.29756 3.27096 2.61252
[221] 2.20188 1.98948 1.67088 1.46556 1.35228
[226] 1.20360 1.00536 0.80004 0.75048 0.56640
[231] 0.51684 0.40356 0.40356 0.34692 0.24780
[236] 0.02124 0.00708 0.14868 0.10620 0.19116
[241] 0.14160 0.12036 0.11328 0.09204 0.05664
[246] 0.03540 0.05664 0.02124 0.06372 0.07080
[251] 0.02124 0.02832 0.04956 0.03540 0.04956
[256] 0.02124 0.01416 0.04956 0.04248 0.04248
[261] 0.02124 0.04956 0.01416 0.03540 0.02832
[266] 0.04248 0.03540 0.02124 0.04956 0.03540
[271] 0.00708 0.05664 0.04248 0.02832 0.03540
[276] 0.03540 0.04248 0.02832 0.02124 0.02832
[281] 0.03540 0.03540 0.03540 0.02124 0.05664
[286] 0.01416 -0.04956 -0.18408 0.14160 0.07788
[291] -0.04956 -0.09912 -0.19824 -0.23364 -0.36108
[296] -0.16284 0.06372 0.14868 0.19116 0.18408
[301] 0.14868 0.11328 0.08496 0.05664 0.07080
[306] 0.04248 0.02832 0.02832 0.02832 0.05664
[311] 0.02832 0.00708 0.04248 0.00708 0.02124
[316] 0.02832 0.02832 0.02832 0.01416 0.02832
[321] 0.01416 0.01416 0.01416 0.02832 0.01416
[326] 0.02124 0.01416 0.00708 0.00000 0.03540
[331] 0.01416 0.02124 0.02124 0.01416 0.00708
[336] 0.01416 0.00708 0.02832 0.01416 0.02124
[341] 0.00000 0.00708 0.00000 0.02832 0.01416
[346] 0.02124 -0.00708 0.02832 -0.00708 0.02124
[351] 0.00708 0.00000 0.00000 0.02124 0.00708
[356] 0.00708 0.01416 0.00708 0.00708 0.02124
[361] 0.00708 0.03540 0.00000 0.00708 0.02124
[366] 0.01416 0.00000 0.02124 0.01416 0.00708
[371] 0.01416 0.00000 0.02832 -0.02124 0.01416
[376] 0.00708 -0.01416 0.00708 0.02124 0.00000
[381] 0.01416 0.00000 0.00708 -0.00708 0.02124
[386] 0.01416 0.02124 0.00708 0.00708 0.01416
[391] 0.00000 0.02124 0.00000 0.00708 0.00000
[396] 0.02124 -0.01416 0.02124 0.00708 0.01416
[401] 0.00708 0.01416 0.00000 0.01416 0.00000
[406] 0.00000 0.02124 0.00708 0.00000 0.00708
[411] 0.00708 0.01416 0.00000 0.00000 0.00000
[416] 0.02124 -0.01416 0.02832 0.00000 0.01416
[421] 0.00708 0.00000 0.00000 0.00000 0.00000
[426] 0.00708 0.01416 0.01416 0.00708 0.01416
[431] 0.01416 0.00708 -0.00708 0.01416 0.00000
[436] 0.00000 0.00708 0.01416 -0.07080 -0.56640
[441] 0.01416 0.21948 0.16284 0.10620 0.05664
[446] 0.04956 0.02124 0.01416 0.01416 0.02124
[451] 0.01416 0.00708 0.00000 0.00708 0.00708
[456] 0.01416 0.02124 0.00000 0.00708 0.00000
[461] 0.00000 0.00000 0.00000 0.01416 0.00708
[466] 0.00708 0.00000 0.00000 0.00000 0.00000
[471] 0.00000 0.00708 0.01416 0.00708 0.00708
[476] 0.00708 0.01416 0.00000 0.00000 0.00000
[481] 0.00000 0.00708 0.00708 0.01416 0.00000
[486] 0.00000 0.00000 0.00000 0.00000 0.01416
[491] 0.00000 0.01416 0.00000 0.00000 0.01416
[496] 0.00708 0.00708 0.00000 0.00000 0.00000
[501] 0.00000 0.00000 0.01416 0.00708 0.00708
[506] 0.00000 0.00000 0.00000 0.00000 0.00000
[511] 0.00000 0.00000 0.00708 0.01416 0.00000
[516] 0.00000 0.00708 0.00000 0.00000 0.00000
[521] 0.00000 0.00000 0.01416 0.01416 0.00000
[526] 0.00000 0.00000 0.00000 0.00000 0.00000
[531] 0.00000 0.00708 0.00000 0.00708 0.00708
[536] 0.00000 0.00000 0.00708 0.00000 0.00000
[541] 0.00000 0.00000 -0.00708 0.00708 0.00000
[546] 0.00000 0.00000 0.00000 0.01416 0.01416
[551] -0.01416 0.00708 0.00000 -0.01416 0.00708
[556] 0.00000 -0.01416 0.00000 -0.02124 -0.00708
[561] 0.00000 0.00000 0.00000 0.00000 0.00000
[566] 0.00000 0.00000 0.02832 0.00000 0.00000
[571] 0.02124 0.00708 0.00000 0.00708 -0.00708
[576] 0.00000 0.01416 -0.00708 -0.01416 -0.02124
[581] -0.00708 -0.01416 -0.00708 0.00000 -0.02124
[586] -0.00708 0.00000 0.00000 0.00000 0.00708
[591] 0.01416 0.00708 0.01416 0.01416 0.00708
[596] 0.02124 0.00000 0.00000 0.00708 0.02124
[601] 0.00000 0.00000 0.00708 0.00708 0.01416
[606] 0.00000 0.00000 0.00000 0.00000 0.00000
[611] 0.00000 0.01416 0.01416 0.00000 0.00000
[616] 0.00000 0.00000 0.00000 0.00000 0.00708
[621] 0.00708 0.01416 0.00000 0.00000 0.00000
[626] 0.00000 0.00000 0.00000 0.00000 0.00000
[631] 0.00000 0.00000 0.00000 0.00000 0.00708
[636] -0.00708 0.02832 -0.00708 0.00708 0.00000
[641] 0.00000 0.00000 0.00000 0.00000 0.00708
[646] -0.00708 0.00000 0.00708 0.00708 0.01416
[651] -0.00708 0.00708 0.00000 0.00000 0.00000
[656] 0.00000 -0.07080 -0.14868 0.00000 0.04248
[661] 0.04956 0.04248 0.02832 0.02124 0.00708
[666] 0.02124 0.00708 0.00000 0.00000 0.01416
[671] 0.00708 0.00708 0.00000 0.00000 0.00000
[676] 0.00000 0.00000 0.00000 0.00000 0.00000
[681] 0.00000 0.00000 0.00000 0.00000 0.00000
[686] 0.00000 0.00000 0.00000 -0.04956 -0.08496
[691] -0.33984 -0.17700 0.19824 0.13452 0.06372
[696] 0.02832 0.02832 0.02832 0.02832 0.02124
[701] 0.02124 0.01416 0.02124 0.00708 0.00000
[706] 0.00708 0.02124 0.00000 0.00000 0.00708
[711] 0.00708 0.01416 -0.00708 -0.01416 0.00708
[716] 0.01416 0.00000 0.00000 0.00000 0.00000
[721] 0.00000 0.00000 0.00000 0.01416 0.00708
[726] 0.00000 0.00708 0.00000 -0.02124 0.00708
[731] 0.01416 0.00000 0.00000 0.00000 0.00000
[736] 0.00000 0.00000 0.00000 0.00000 0.00000
[741] 0.02124 0.00708 0.00000 0.00000 0.00000
[746] 0.00000 -0.02124 -0.02124 -0.06372 -0.08496
[751] -0.10620 -0.14868 -0.46020 -0.33984 -0.02124
[756] 0.09912 0.19116 0.15576 0.12744 0.09912
[761] 0.07788 0.07788 0.05664 0.03540 0.03540
[766] 0.02832 0.02832 0.02124 0.02124 0.01416
[771] 0.02124 0.00708 0.02832 0.00000 0.00708
[776] 0.02124 0.00000 0.00000 0.00708 0.02124
[781] 0.00000 0.00000 0.00000 0.02124 0.00708
[786] 0.00000 0.00000 0.00000 0.00000 0.00708
[791] 0.01416 0.00708 0.00000 0.00000 0.00000
[796] 0.00000 0.00000 0.00000 0.00000 0.00000
[801] 0.00000 0.00708 0.00000 0.02124 -0.01416
[806] 0.00708 0.00708 0.00000 0.00000 0.00000
[811] 0.00000 0.00000 0.00000 0.00000 0.00708
[816] 0.00000 -0.02124 -0.08496 -0.04248 0.00000
[821] 0.00000 0.00000 0.00000 0.00000 0.00000
[826] 0.00000 0.00000 0.00000 0.01416 0.00000
[831] -0.00708 0.02124 -0.02124 -0.03540 0.02832
[836] 0.02832 0.00000 0.00708 -0.00708 0.02124
[841] -0.01416 -0.00708 0.00000 -0.01416 -0.04956
[846] -0.02124 -0.02832 -0.03540 -0.03540 -0.04956
[851] -0.12036 -0.07080 -0.14160 -0.09912 -0.01416
[856] 0.04956 0.04956 0.06372 0.05664 0.00000
[861] 0.00708 0.00000 0.00708 0.01416 0.00708
[866] -0.00708 0.00000 0.00708 0.00000 0.00000
[871] 0.00000 0.00708 0.01416 0.02124 0.01416
[876] 0.01416 0.01416 0.00708 0.02124 0.00000
[881] 0.00708 -0.00708 -0.04956 -0.03540 -0.47436
[886] -2.93112 -4.42500 1.09032 0.65844 0.18408
[891] -1.51512 -5.38788 -10.78992 -15.70344 -2.61960
[896] 6.15960 6.93840 4.41792 4.63032 3.46920
[901] 2.68332 1.99656 1.49388 1.43724 2.80368
[906] -0.43896 0.39648 0.50976 0.41772 0.29736
[911] 0.30444 0.23364 0.16992 0.03540 0.11328
[916] 0.17700 0.13452 0.16284 0.16992 0.07080
[921] 0.09204 0.12744 0.07080 0.08496 0.09912
[926] 0.10620 0.07788 0.07788 0.07080 0.07788
[931] 0.07788 0.09912 0.07080 0.04248 0.07080
[936] 0.08496 0.06372 0.05664 0.04956 0.04956
[941] 0.04248 0.04956 0.04248 0.04956 0.02832
[946] 0.04956 0.01416 0.02832 0.04248 0.03540
[951] -0.02832 -0.00708 0.01416 -0.04248 -0.22656
[956] -0.43188 -0.77880 -0.53100 -0.30444 -0.81420
[961] -1.50096 -1.16820 -0.21240 -0.11328 0.22656
[966] 0.46020 0.49560 0.46728 0.47436 0.41064
[971] 0.58764 0.67968 0.36108 0.23364 0.15576
[976] 0.13452 0.07788 0.12744 0.07788 0.12036
[981] 0.08496 0.06372 0.07080 0.10620 0.02124
[986] -0.00708 0.08496 0.07080 0.03540 0.05664
[991] 0.02124 0.06372 0.05664 0.04956 0.04248
[996] 0.03540 0.03540 0.04956 0.02124 0.04248
[ reached getOption("max.print") -- omitted 25280 entries ]
length(ChangeRF(as.numeric(hilomodified$cms)))
[1] 26280
length(hilomodified$cms) # same length
[1] 26280
hilomodified$changeCMS <- change.vector
head(hilomodified)
# 10-11 negative start of storm
# 11-10 positive end of storm
positive.change <- vector()
negative.change <- vector()
index <- vector()
for(i in 1:length(hilomodified$cms)){
if (hilomodified$changeCMS[i] < 0 ){
negative.change[i] <- i
}else{
if(hilomodified$changeCMS[i] > 0){
postive.change[i] <- i
}
}
if(hilomodified$cms[i] >= 10 ){
index[i] <- i
}
}
index
positive.change
negative.change
length(which(index > 0))
storms <- sort(c(positive.change,negative.change), decreasing = FALSE)
storms
hilostorms <- hilomodified[storms,]
hilostorms
storm <- ggplot(hilostorms[50:250,], aes(x = date, y = as.numeric(cms))) +
geom_line(color="black")+
geom_line(aes(y = as.numeric(Corr.NTU)), color="grey69") +
geom_line(aes(y=as.numeric(ChlRFU)),color="khaki")
print(storm + ggtitle("Storm 1/7/13")+labs(x="Time"))

vector <- vector()
for(i in 1:length(storms)){
if(isTRUE(abs(storms[i]-storms[i+1]) > 10)){
stop <- storms[i]
}else{
stop <- 0
}
if(stop > 0){
vector[i] <- stop
}
}
vector[which(is.na(vector)==FALSE)]
[1] 622 672 1706 1898 1924 1970 2081 2327 2388
[10] 2429 2469 2493 2515 2537 2576 2714 2734 2772
[19] 2799 2825 2866 2947 2998 3051 3068 3130 3196
[28] 3211 3226 3249 3373 3746 3841 3856 3898 4343
[37] 4412 4438 4460 4507 4534 4617 4660 4827 5006
[46] 5087 5107 5135 5237 5667 5690 5735 5767 5786
[55] 5837 5851 5888 5900 5921 5968 6048 6068 6092
[64] 6240 6270 6284 6320 6363 6379 6456 6488 6599
[73] 6630 6864 6888 6908 6945 6959 6977 6999 7025
[82] 7063 7076 7099 7134 7150 7222 7308 7443 7466
[91] 7493 7678 7703 7733 7786 7805 7840 7863 7882
[100] 7933 7960 7978 8005 8031 8124 8167 8954 9013
[109] 9067 9091 9135 9160 9188 9256 9280 9303 9333
[118] 9377 9487 9505 10205 12119 12216 12238 13083 13095
[127] 14759 14786 14812 14831 14891 14946 14998 15028 15079
[136] 15099 15125 15140 15165 15187 15209 15526 15540 15553
[145] 15702 17365 17482 17512 17533 17585 17612 17657 17708
[154] 17734 17778 17811 17838 17871 17925 17978 18210 18233
[163] 18358 18888 18905 18921 19117 19192 19225 19269 19322
[172] 19342 19362 19378 19490 19504 19522 19628 21792 21813
[181] 21948 21981 22016 22075 22124 22220 23568
storm.1.7.13 <- ggplot(hilomodified[1:80,], aes(x = date, y = as.numeric(cms))) +
geom_line(color="black")+
geom_line(aes(y = as.numeric(Corr.NTU)), color="grey69") +
geom_line(aes(y=as.numeric(ChlRFU)),color="khaki")
print(storm.1.7.13 + ggtitle("Storm 1/7/13")+labs(x="Time"))

---
title: "Data Cleaning & Descriptives"
author: "Brianna Cirillo & Odalys Barrientos"
output: html_notebook
---
# Matching the bouy data to the other data collected 
```{r}
hilo <- hbb_wku_h_xts
hilo <- data.frame(date=index(hilo), coredata(hilo))
hilo <- hilo[529:54816,]
hilo
# which(hilo$date=="2010-10-23 00:00:00") = index 529 

# which(hilo$date=="2016-12-31 23:00:00") = index 54816
```
```{r}
length(hilo[,1]) # we are left with 54288 lines of data

54816 - length(hilo[,1]) # we lost 528 values 
```

# Changing column names 
```{r}
# removing columns that we are not using 
hilo$date.2 <- NULL # another date column
hilo$date.1 <- NULL # another date column
hilo$BGARFU <- NULL # ?
hilo$cfs <- NULL
hilo$DOmgL <- NULL # dissolved oxygen
#hilo$Doper <- NULL # dissolved oxygen
hilo$PAR1 <- NULL # ?
hilo$pH <- NULL # pH
hilo$NTU <- NULL # a different measurement for turbitity
hilo$DOper10 <- NULL # dissolved oxygen

# colnames(hilo) <- c("Date", "cfs", "RiverFlow-cumec", "LogRiverFlow-cumec", "Chlorophyll-RFU", "Salinity-PPT", "Temp-C", "chlorophyll-calibrator", "Turbidity-NTU")
# does not work ???

hilo
```

====================================================

# FULL DATA SET 2012-2016
# Descriptives: PLots

## River Flow FULL DATA SET
```{r}
length(hilo$logcms[which(is.na(hilo$logcms)==TRUE)]) # 12 NAs 
which(is.na(hilo$logcms)==TRUE)

RiverFlow <- ggplot(hilo,  aes(x = date, y = as.numeric(cms))) + 
  geom_line()

print(RiverFlow + ggtitle("River Flow")+labs(x="Time", y = "River Flow - cubic meters per second"))
```
## CHL FULL DATA SET
```{r}
length(hilo$ChlRFU[which(is.na(hilo$ChlRFU)==TRUE)]) # 20464 NAs

which(as.numeric(hilo$ChlRFU)==max(as.numeric(na.omit(hilo$ChlRFU)))) # 15.3 max 
# CHL tells us where in the data set this happened  
hilo[38974,]

CHL <- ggplot(hilo,  aes(x = date, y = as.numeric(ChlRFU))) + 
  geom_line()

print(CHL + ggtitle("Chlorophyll ")+labs(x="Time", y = "Chlorophyll  - relative fluorescence units (RFU)"))
```
## Turbity FULL DATA SET
```{r}
length(hilo$Corr.NTU[which(is.na(hilo$Corr.NTU)==TRUE)]) #15012 NAs

which(as.numeric(hilo$Corr.NTU)==max(as.numeric(na.omit(hilo$Corr.NTU)))) # 88.4
# tells us where in the data set this happened
hilo[33243,]

TURB <- ggplot(hilo,aes(x = date, y = as.numeric(Corr.NTU))) + 
  geom_line()

print(TURB + ggtitle("Turbidity ")+labs(x="Time", y = "Turbidity - Nephelometric Turbidity Units (NTU)"))
```
## Salinity FULL DATA SET
```{r}
length(hilo$saltppt[which(is.na(hilo$saltppt)==TRUE)]) #11330 NAs

SALT <- ggplot(hilo,  aes(x = date, y = as.numeric(saltppt))) + 
  geom_line()

print(SALT + ggtitle("Salinity")+labs(x="Time", y = "Salinity - unit parts per thousand (PPT)"))
```

========================================================
# Histograms FULL DATA SET
## River Flow Histogram
```{r}
hist(as.numeric(hilo$logcms), main = "Histogram of Log River Flow", xlab = "Log River Flow")

# this looks okay
```
## CHL Histogram
```{r}
# VERY skewed
hist(as.numeric(hilo$ChlRFU), main = "Histogram of Chlorophyll", xlab = "Chlorophyll  - relative fluorescence units (RFU)")

# this looks better
hist(log(as.numeric(hilo$ChlRFU)), main = "Histogram of Log Chlorophyll", xlab = "Chlorophyll")
# not sure what happens to units when taking the log 
```
## Turbity Histogram
```{r}
# VERY skewed
hist(as.numeric(hilo$Corr.NTU), main = "Histogram of Turbidity", xlab = "Turbidity - Nephelometric Turbidity Units (NTU)")

# this looks better
hist(log(as.numeric(hilo$Corr.NTU)), main = "Histogram of Log Turbidity", xlab = "Turbidity")
# not sure what happens to units when taking the log 
```
## Salinity Histogram
```{r}
# skewed
hist(as.numeric(hilo$saltppt), main = "Histogram of Salinity", xlab = "Salinity - unit parts per thousand (PPT)")

# this is worst!
hist(log(as.numeric(hilo$saltppt)), main = "Histogram of Log Salinity", xlab = "Salinity")
# not sure what happens to units when taking the log 
```

=========================================================
# NEW DATA SET-Modified Data 2013-2015
```{r}
# start date: 2013-01-01 00:00:00
# end date: 2015-12-31 23:00:00
hilomodified <- hilo[19225:45504,]
length(hilo[,1])-length(hilomodified[,1])
# lost 28008 entries of data 

length(hilomodified[,1])-528 # we are left with 25752 entries of data 


head(hilomodified)
tail(hilomodified)

```

# Descriptives on all variables MODIFIED DATA SET: Using Favstats
## River Flow Favstats
```{r}
library(mosaic)
favstats(hilomodified$cms)
```
## CHL Favstats
```{r}
favstats(hilomodified$ChlRFU)
```
## Turbitity Favstats
```{r}
favstats(hilomodified$Corr.NTU)
```
## Salinity Favstats
```{r}
favstats(hilomodified$saltppt)
favstats(hilomodified$TempC)
favstats(hilomodified$Doper)
```

=====================================================
# MODIFIED DATA SET 2013-2015
# Descriptives: Plots

## River Flow MODIFIED
```{r}
length(hilomodified$logcms[which(is.na(hilomodified$logcms)==TRUE)]) # No NAs, YAY!
which(is.na(hilomodified$logcms)==TRUE)

RiverFlowMod <- ggplot(hilomodified,  aes(x = date, y = as.numeric(cms))) + 
  geom_line()

print(RiverFlowMod + ggtitle("River Flow")+labs(x="Time", y = "River Flow - cubic meters per second"))
```
## CHL MODIFIED
```{r}
sum(is.na(hilomodified$ChlRFU)==TRUE) # 1884 NAs
#which(is.na(hilomodified$ChlRFU)==TRUE)
# max(as.numeric(na.omit(hilo$ChlRFU))) # 15.3
which(as.numeric(hilomodified$ChlRFU)==15.3)
hilo[38974,]

CHLMod <- ggplot(hilomodified,  aes(x = date, y = as.numeric(ChlRFU))) + 
  geom_line()

print(CHLMod + ggtitle("Chlorophyll ")+labs(x="Time", y = "Chlorophyll  - relative fluorescence units (RFU)"))
```


# Turbitity MODIFIED
```{r}
length(hilomodified$Corr.NTU[which(is.na(hilomodified$Corr.NTU)==TRUE)]) # 2704 NAs
# max(as.numeric(na.omit(hilo$Corr.NTU))) # 88.4
which(as.numeric(hilo$Corr.NTU)==88.4)
hilo[33243,]

TURBMod <- ggplot(hilomodified,  aes(x = date, y = as.numeric(Corr.NTU))) + 
  geom_line()

print(TURBMod + ggtitle("Turbidity ")+labs(x="Time", y = "Turbidity - Nephelometric Turbidity Units (NTU)"))
```

# Salinity MODIFIED
```{r}
length(hilomodified$saltppt[which(is.na(hilomodified$saltppt)==TRUE)]) # 2267 NAs

SALTMod <- ggplot(hilomodified,  aes(x = date, y = as.numeric(saltppt))) + 
  geom_line()

print(SALTMod + ggtitle("Salinity")+labs(x="Time", y = "Salinity - unit parts per thousand (PPT)"))
```
# Tempurature MODIFIED
```{r}
length(hilomodified$TempC[which(is.na(hilomodified$TempC)==TRUE)]) # 2267 NAs

TempMod <- ggplot(hilomodified,  aes(x = date, y = as.numeric(TempC))) + 
  geom_line()

print(TempMod + ggtitle("Temperature")+labs(x="Time", y = "Temperature - Celsius"))
```
# Dissolved Oxygen MODIFIED
```{r}
length(hilomodified$Doper[which(is.na(hilomodified$Doper)==TRUE)]) # 2267 NAs

TempMod <- ggplot(hilomodified,  aes(x = date, y = as.numeric(Doper))) + 
  geom_line()

print(TempMod + ggtitle("Dissolved Oxygen")+labs(x="Time", y = "Dissolved Oxygen in percent of saturation"))
```
===================================================
# Histograms MODIFIED 

## River Flow MODIFIED
```{r}
hist(as.numeric(hilomodified$cms), main = "Histogram of Log River Flow", xlab = "Log River Flow", breaks =90, xlim = c(0,100))

# this looks okay
```
## CHL MODIFIED
```{r}
# VERY skewed
hist(as.numeric(hilomodified$ChlRFU), main = "Histogram of Chlorophyll", xlab = "Chlorophyll  - relative fluorescence units (RFU)")

# this looks better
hist(log(as.numeric(hilomodified$ChlRFU)), main = "Histogram of Log Chlorophyll", xlab = "Chlorophyll")
# not sure what happens to units when taking the log 
```
## Turbitity MODIFIED
```{r}
# VERY skewed
hist(as.numeric(hilomodified$Corr.NTU), main = "Histogram of Turbidity", xlab = "Turbidity - Nephelometric Turbidity Units (NTU)")

# this looks better
hist(log(as.numeric(hilomodified$Corr.NTU)), main = "Histogram of Log Turbidity", xlab = "Turbidity")
# not sure what happens to units when taking the log 
```
## Salinity MODIFIED
```{r}
# skewed
hist(as.numeric(hilomodified$saltppt), main = "Histogram of Salinity", xlab = "Salinity - unit parts per thousand (PPT)")

# this is worst!
hist(log(as.numeric(hilomodified$saltppt)), main = "Histogram of Log Salinity", xlab = "Salinity")
# not sure what happens to units when taking the log 
```
=============================================================

# Plot with ALL Var 2013-2015
It is hard to see what is going on 
```{r}
AllYears <- ggplot(hilomodified,  aes(x = date, y = as.numeric(logcms))) + 
  geom_line()+
  geom_line(aes(y = as.numeric(saltppt)), color = "darkred") +
  geom_line(aes(y = as.numeric(Corr.NTU)), color="darkgreen") +
  geom_line(aes(y=as.numeric(ChlRFU)),color="blue")

print(AllYears + ggtitle("Hilo Bay")+labs(x="Time", y = "River Flow - cubic meters per second"))

```


==============================================================
# Descriptives by Storm
We are picking one storm from each year. We can indicate a storm has occurred by the extreme events in the river flow data. 
We will not use the log (which is logbase10) in order to see the extreme events
When salinity is below 35 this also indicates a storm has occurred. 

We will break the data set by year to find the most extreme event for each year. 
```{r}
hilo2013 <- hilomodified[1:8760,]

hilo2014 <- hilomodified[8761:17520,]

hilo2015 <- hilomodified[17521:length(hilomodified[,1]),]
```

# 2013 Data & Plot
```{r}
# max(as.numeric(hilo2013$logcms)) # this is log base 10 
# This is NOT the natural log
max(as.numeric(hilo2013$cms)) # 207.186 
which(as.numeric(hilo2013$cms) == max(as.numeric(hilo2013$cms)))

hilo2013[3550,]
# all values for this storm are NA

plot2013 <- ggplot(hilo2013,  aes(x = date, y = as.numeric(cms))) + 
  geom_line(color="black")+
  geom_line(aes(y = as.numeric(saltppt)), color = "darkred") +
  geom_line(aes(y = as.numeric(Corr.NTU)), color="darkgreen") +
  geom_line(aes(y=as.numeric(ChlRFU)),color="blue")

print(plot2013 + ggtitle("2013")+labs(x="Time", y = "River Flow - cubic meters per second"))
```

#### Split 2013 into 6 months to get a better visual
```{r}
# R2013.1 <- ggplot(hilo2013[1:(length(hilo2013[,1])/2),],  aes(x = date, y = as.numeric(cms))) + 
#   geom_line(color="black")+
#   geom_line(aes(y = as.numeric(saltppt)), color = "darkred") +
#   geom_line(aes(y = as.numeric(Corr.NTU)), color="darkgreen") +
#   geom_line(aes(y=as.numeric(ChlRFU)),color="blue")
# print(R2013.1 + ggtitle("River Flow")+labs(x="Time", y = "River Flow - cubic meters per second"))
# R2013.1+ylim(0,40)
# 
# 
# R2013.2 <- ggplot(hilo2013[(length(hilo2013[,1])/2):length(hilo2013[,1]),],  aes(x = date, y = as.numeric(cms))) + 
#   geom_line(color="black")+
#   geom_line(aes(y = as.numeric(saltppt)), color = "darkred") +
#   geom_line(aes(y = as.numeric(Corr.NTU)), color="darkgreen") +
#   geom_line(aes(y=as.numeric(ChlRFU)),color="blue")
# print(R2013.2 + ggtitle("River Flow")+labs(x="Time", y = "River Flow - cubic meters per second"))
# R2013.2+ylim(0,40)
```

# 2014 Data & Plot
```{r}
# Max river flow in the overall data set
max(as.numeric(hilo2014$cms))
max(as.numeric(hilomodified$cms))

which(as.numeric(hilo2014$cms) == max(as.numeric(hilo2014$cms)))

hilo2014[5263,]

R2014 <- ggplot(hilo2014,  aes(x = date, y = as.numeric(logcms))) + 
  geom_line(color="black")+
  geom_line(aes(y = as.numeric(saltppt)), color = "darkred") +
  geom_line(aes(y = as.numeric(Corr.NTU)), color="darkgreen") +
  geom_line(aes(y=as.numeric(ChlRFU)),color="blue")

print(R2014 + ggtitle("2014")+labs(x="Time", y = "River Flow - cubic meters per second"))
```


# 2015 Data & Plot
```{r}
max(as.numeric(hilo2015$cms))

which(as.numeric(hilo2015$cms) == max(as.numeric(hilo2015$cms)))

hilo2015[6475,]

R2015 <- ggplot(hilo2015,  aes(x = date, y = as.numeric(logcms))) + 
  geom_line(color="black")+
  geom_line(aes(y = as.numeric(saltppt)), color = "darkred") +
  geom_line(aes(y = as.numeric(Corr.NTU)), color="darkgreen") +
  geom_line(aes(y=as.numeric(ChlRFU)),color="blue")

print(R2015 + ggtitle("2015")+labs(x="Time", y = "River Flow - cubic meters per second"))
```


# Separating the Data by Storm Events
=======
# Separating the Data by Storm

```{r}
outcomes <- as.numeric(hilomodified$saltppt)<25 
index.lt25 <- which(outcomes==TRUE)

poss.storms <- hilomodified[index.lt25,]
poss.storms
```


```{r}
outcomes.35 <- as.numeric(hilomodified$saltppt)<35 
index.lt35 <- which(outcomes==TRUE)
index.lt25
poss.storms35 <- hilomodified[index.lt25,]
poss.storms35

hilomodified$date[16]
```

```{r}
storm.1.7.13 <- ggplot(hilomodified[145:168,],  aes(x = date, y = as.numeric(cms))) + 
  geom_line(color="black")+
  geom_line(aes(y = as.numeric(saltppt)), color = "lightsteelblue1") +
  geom_line(aes(y = as.numeric(Corr.NTU)), color="grey69") +
  geom_line(aes(y=as.numeric(ChlRFU)),color="khaki")

print(storm.1.7.13 + ggtitle("Storm 1/7/13")+labs(x="Time"))
```

# Trying to make the Rainfall plot easier to read

```{r}
RiverFlow1 <- ggplot(hilomodified[1:100,],  aes(x = date, y = as.numeric(cms))) + 
  geom_line()

print(RiverFlow + ggtitle("River Flow")+labs(x="Time", y = "River Flow - cubic meters per second"))
```
=======
```{r}
getwd()
# write.csv(hilomodified,file="HiloBayNEW13to15.csv", row.names = FALSE)

```

# NEW Change in River Flow Column 

```{r}
pos.neg <- as.numeric(hilomodified$cms) - 10

start <- vector()
end <- vector()

for(i in 1:length(pos.neg)){
  if(isTRUE(pos.neg[i] < 0 && pos.neg[i+1] > 0)){
    start[i] <- i
  }else if(isTRUE(pos.neg[i] > 0 && pos.neg[i+1] < 0)){
    end[i] <- i
    }
}
start
start<-start[!is.na(start)]
length(start)
end
end<-end[!is.na(end)]
length(end)
```


```{r}
for(i in 1:length(start)){
  storm <- ggplot(hilomodified[(start[i]-24):(end[i]+24),],  aes(x = date, y = as.numeric(cms))) +
  geom_line(color="black")+
  geom_line(aes(y = as.numeric(Corr.NTU)), color="grey69") +
  geom_line(aes(y=as.numeric(ChlRFU)),color="khaki")

print(storm + ggtitle("Storm 1/7/13")+labs(x="Time"))
}

```

```{r}
ChangeRF <- function(x = vector()){
change <- c(0)
  for(i in 1:(length(x)-1)){
    change[i+1] <- x[i]-x[i+1]
  }
  return(change)
}
change.vector <- c(ChangeRF(as.numeric(hilomodified$cms)))
change.vector
length(ChangeRF(as.numeric(hilomodified$cms)))
length(hilomodified$cms) # same length

hilomodified$changeCMS <- change.vector

head(hilomodified)
```

```{r}
# 10-11 negative start of storm
# 11-10 positive end of storm

positive.change <- vector()
negative.change <- vector()
index <- vector()


for(i in 1:length(hilomodified$cms)){
  if (hilomodified$changeCMS[i] < 0 ){
    negative.change[i] <- i
  }else{
    if(hilomodified$changeCMS[i] > 0){
      postive.change[i] <- i
    }
  }
  if(hilomodified$cms[i] >= 10 ){
    index[i] <- i
  }
}
index
positive.change
negative.change
length(which(index > 0)) 

storms <- sort(c(positive.change,negative.change), decreasing = FALSE)
storms
hilostorms <- hilomodified[storms,]
hilostorms
```

```{r}
storm <- ggplot(hilostorms[50:250,],  aes(x = date, y = as.numeric(cms))) +
  geom_line(color="black")+
  geom_line(aes(y = as.numeric(Corr.NTU)), color="grey69") +
  geom_line(aes(y=as.numeric(ChlRFU)),color="khaki")

print(storm + ggtitle("Storm 1/7/13")+labs(x="Time"))
```

```{r}

vector <- vector()
for(i in 1:length(storms)){
  if(isTRUE(abs(storms[i]-storms[i+1]) > 10)){
    stop <- storms[i]
  }else{
    stop <- 0
  }
  if(stop > 0){
    vector[i] <- stop
  }
}
vector[which(is.na(vector)==FALSE)]

storm.1.7.13 <- ggplot(hilomodified[1:80,],  aes(x = date, y = as.numeric(cms))) +
  geom_line(color="black")+
  geom_line(aes(y = as.numeric(saltppt)), color = "lightsteelblue1") +
  geom_line(aes(y = as.numeric(Corr.NTU)), color="grey69") +
  geom_line(aes(y=as.numeric(ChlRFU)),color="khaki")

print(storm.1.7.13 + ggtitle("Storm 1/7/13")+labs(x="Time"))
```

```{r}
startaxis <- vector()
endaxis <- vector()
i <- 1
while (i <= length(change.vector)) {
  if(isTRUE(change.vector[i] < 0)){
    startaxis[i] <- RiverFlow[i]
  }
  i <- i+1
}
begin<-which(is.na(startaxis) == FALSE)
begin

while (i <= length(change.vector)) {
  if(isTRUE(change.vector[i] > 0)){
    endaxis[i] <- RiverFlow[i]
  }
  i <- i+1
}
end <- which(is.na(endaxis)==FALSE)


storm.1.7.13 <- ggplot(hilomodified[begin,],  aes(x = date, y = as.numeric(cms))) + 
  geom_line(color="black")+
  geom_line(aes(y = as.numeric(Corr.NTU)), color="grey69") +
  geom_line(aes(y=as.numeric(ChlRFU)),color="khaki")

print(storm.1.7.13 + ggtitle("Storm 1/7/13")+labs(x="Time"))

```

```{r}
while (i <= length(change.vector)) {
  if(isTRUE(change.vector[i] < 0)){
    startaxis[i] <- RiverFlow[i]
    i <- i+1
  }else{
    if(isTRUE(change.vector[i] > 0)){
      endaxis[i] <- RiverFlow[i]
    }
  }
}
```

